Target-based rhythmic pattern generation and variation with genetic algorithms

Abstract

Composing drum patterns and musically developing them through repetition and variation is a typical task in electronic music production. We propose a system that, given an input pattern, automatically creates related patterns using a genetic algorithm. Two distance measures (the Hamming distance and directed-swap distance) that relate to rhythmic similarity are shown to derive usable fitness functions for the algorithm. A software instrument in the Max for Live environment presents how this can be used in real musical applications. Finally, a user survey was carried out to examine and compare the effectiveness of the fitness metrics in determining rhythmic similarity as well as the usefulness of the instrument for musical creation.